A network of molecular interactions, involving many thousands of genes, their products, and other molecules, underlie cellular processes. Investigation of these interactions across a wide range of scales ranging from the formation/activation of transcriptional complexes, to the availability of a signaling pathway, all the way to macroscopic processes, such as cell adhesion, calls for a new level of sophistication in the design of genomewide computational approaches. A homogeneous environment for the comprehensive mapping and analysis of molecular cellular interactions in would be a powerful resource for the biomedical research community. We propose the creation of a National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet). The Center will provide an integrative computational framework to organize molecular interactions in the cell into manageable context-dependent components and will develop interoperable computational models and tools that can leverage such a map of cellular interactions to elucidate important biological processes. Center activities will involve a significant, multidisciplinary effort of biological and computational sciences. Specific areas of expertise include natural language parsing (NLP), machine learning (ML), software systems and engineering, databases, computational structural biology, reverse engineering of genetic networks, biomedical literature datamining, and biomedical ontologies, among others. The Center will 1) construct an evidence integration framework to collect and fuse a variety of diverse cellular interaction clues based on their statistical relevance 2) assemble a comprehensive set of physics- and knowledge-based methodologies to fill this framework 3) provide a set of methodologies and filters, anchored in formal domain ontologies, to associated specific interactions to an organism, tissue, molecular, and cellular context. All relevant tools will be made accessible to the biomedical research community through a common, extensible, and interoperable software platform, geWorkbench. We will reach out to train and encourage researchers to use and/or develop new modules for, geWorkbench. An important element of the software platform will be the development of specific components that can exploit the evidence integration techniques developed by Core 1 investigators to combine molecular interaction clues from Core 2 algorithms and databases. Development will be both driven and tested by the biomedical community to ensure the usefulness of the tools and the usability of the graphical user interfaces to address biomedical problems in completely novel ways, to dissect the web of cellular interactions responsible for cellular processes and functions.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
3U54CA121852-04S1
Application #
7674889
Study Section
Special Emphasis Panel (ZRG1-BST-A (55))
Program Officer
Ogunbiyi, Peter
Project Start
2005-09-26
Project End
2010-07-31
Budget Start
2008-08-15
Budget End
2009-07-31
Support Year
4
Fiscal Year
2008
Total Cost
$113,826
Indirect Cost
Name
Columbia University (N.Y.)
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Hui, Ken Y; Fernandez-Hernandez, Heriberto; Hu, Jianzhong et al. (2018) Functional variants in the LRRK2 gene confer shared effects on risk for Crohn's disease and Parkinson's disease. Sci Transl Med 10:
Azad, Robert N; Zafiropoulos, Dana; Ober, Douglas et al. (2018) Experimental maps of DNA structure at nucleotide resolution distinguish intrinsic from protein-induced DNA deformations. Nucleic Acids Res 46:2636-2647
Abe, Takayuki; Lee, Albert; Sitharam, Ramaswami et al. (2017) Germ-Cell-Specific Inflammasome Component NLRP14 Negatively Regulates Cytosolic Nucleic Acid Sensing to Promote Fertilization. Immunity 46:621-634
Alvarez, Mariano J; Shen, Yao; Giorgi, Federico M et al. (2016) Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 48:838-47
Sheng, Ren; Jung, Da-Jung; Silkov, Antonina et al. (2016) Lipids Regulate Lck Protein Activity through Their Interactions with the Lck Src Homology 2 Domain. J Biol Chem 291:17639-50
Lachmann, Alexander; Giorgi, Federico M; Lopez, Gonzalo et al. (2016) ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32:2233-5
Park, Mi-Jeong; Sheng, Ren; Silkov, Antonina et al. (2016) SH2 Domains Serve as Lipid-Binding Modules for pTyr-Signaling Proteins. Mol Cell 62:7-20
Baskovich, Brett; Hiraki, Susan; Upadhyay, Kinnari et al. (2016) Expanded genetic screening panel for the Ashkenazi Jewish population. Genet Med 18:522-8
Bisikirska, Brygida; Bansal, Mukesh; Shen, Yao et al. (2016) Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression. Cancer Res 76:664-74
Del Giudice, Ilaria; Marinelli, Marilisa; Wang, Jiguang et al. (2016) Inter- and intra-patient clonal and subclonal heterogeneity of chronic lymphocytic leukaemia: evidences from circulating and lymph nodal compartments. Br J Haematol 172:371-383

Showing the most recent 10 out of 258 publications